Learning the Relevant Percepts of Modular Hierarchical Bayesian Driver Models Using a Bayesian Information Criterion

  • Mark Eilers
  • Claus Möbus
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6777)


Modeling drivers’ behavior is essential for the rapid prototyping of error-compensating assistance systems. Various authors proposed control-theoretic and production-system models. Based on psychological studies various perceptual measures (angles, distances, time-to-x-measures) have been proposed for such models. These proposals are partly contradictory and depend on special experimental settings. A general computational vision theory of driving behavior is still pending. We propose the selection of drivers’ percepts according to their statistical relevance. In this paper we present a new machine-learning method based on a variant of the Bayesian Information Criterion (BIC) using a parent-child-monitor to obtain minimal sets of percepts which are relevant for drivers’ actions in arbitrary scenarios or maneuvers.


Probabilistic Driver model Bayesian Autonomous Driver model Mixture-of-Behavior model Bayesian Real-Time-Control Machine-Learning Bayesian Information Criterion Hierarchical Bayesian Models 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bessière, P., Laugier, C., Siegwart, R. (eds.): Probabilistic Reasoning and Decision Making in Sensory-Motor Systems. Springer, Berlin (2008)zbMATHGoogle Scholar
  2. 2.
    Cacciabue, P.C.: Modelling Driver Behaviour in Automotive Environments. Springer, London (2007)CrossRefGoogle Scholar
  3. 3.
    Chattington, M., Wilson, M., Ashford, D., Marple-Horvat, D.E.: Eye-Steering Coordination in Natural Driving. Experimental Brain Research 180, 1–14 (2007)CrossRefGoogle Scholar
  4. 4.
    Cowell, R.G., Dawid, A.P., Lauritzen, S.L., Spiegelhalter, D.J.: Probabilistic Networks and Expert Systems. Springer, Berlin (1999)zbMATHGoogle Scholar
  5. 5.
    Eilers, M., Möbus, C.: Learning of a Bayesian Autonomous Driver Mixture-of-Behaviors (BAD-MoB) Model. In: Duffy, V.G. (ed.) Advances in Applied Digital Human Modeling, pp. 436–445. CRC Press, Taylor & Francis Group, Boca Raton (2010/2011)Google Scholar
  6. 6.
    Eilers, M., Möbus, C.: Lernen eines modularen Bayesian Autonomous Driver Mixture-of-Behaviors (BAD MoB) Modells. In: Jürgensohn, T., Kolrep, H. (eds.) Fahrermodellierung in Wissenschaft und Wirtschaft, 3. Berliner Fachtagung für Fahrermodellierung, Fortschrittsbericht des VDI in der Reihe 22 (Mensch-Maschine-Systeme), pp. 61–74. VDI-Verlag (2010)Google Scholar
  7. 7.
    Horrey, W.J., et al.: Modeling Driver’s Visual Attention Allocation While Interacting With In-Vehicle Technologies. J. Exp. Psych. 12, 67–78 (2006)Google Scholar
  8. 8.
    Jensen, F.V., Nielsen, T.D.: Bayesian Networks and Decision Graphs, 2nd edn. Springer, Heidelberg (2007)CrossRefzbMATHGoogle Scholar
  9. 9.
    Land, M.F.: The Visual Control of Steering. In: Harris, L.R., Jenkin, M. (eds.) Vision and Action, pp. 163–180. Cambridge University Press, Cambridge (1998)Google Scholar
  10. 10.
    Land, M., Horwood, J.: Which Parts of the Road Guide Steering? Nature 377, 339–340 (1995)CrossRefGoogle Scholar
  11. 11.
    Land, M., Lee, D.N.: Where we look when we steer. Nature 369, 742–744 (1994)CrossRefGoogle Scholar
  12. 12.
    Lee, D.N.: A theory of visual control of braking based on information about time-to-collision. Perception 5, 437–459 (1976)CrossRefGoogle Scholar
  13. 13.
  14. 14.
    Möbus, C., Eilers, M.: Further Steps Towards Driver Modeling according to the Bayesian Programming Approach. In: Salvendy, G., Smith, M.J. (eds.) HCI International 2009. LNCS (LNAI), vol. 5618, pp. 413–422. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  15. 15.
    Möbus, C., Eilers, M.: Mixture of Behaviors and Levels-of-Expertise in a Bayesian Autonomous Driver Model. In: Duffy, V.G. (ed.) Advances in Applied Digital Human Modeling, pp. 425–435. CRC Press, Taylor & Francis Group, Boca Raton (2010/2011)Google Scholar
  16. 16.
    Möbus, C., Eilers, M., Garbe, H.: Prediction of Deficits in Situation Awareness with a Modular Hierarchical Bayesian Driver Model, HCII 2011 (submitted; this volume) (2011)Google Scholar
  17. 17.
    Pepping, G.J., Grealy, M.A.: Closing the Gap: The Scientific Writings of David N. Lee. Lawrence Erlbaum Associates, Publishers, Mahwah (2007)Google Scholar
  18. 18.
    Schwarz, G.: Estimating the Dimension of a Model. The Annals of Statistics 6(2), 461–464 (1978)MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Wilkie, R.M., Wann, J.P.: Driving as night falls: The contribution of retinal flow and visual direction to the control of steering. Current Biology 12, 2014–2017 (2002)CrossRefGoogle Scholar
  20. 20.
    Wilkie, R.M., Wann, J.P.: Eye-Movements Aid the Control of Locomotion. Journal of Vision 3, 677–684 (2003)CrossRefGoogle Scholar
  21. 21.
    Xu, Y., Lee, K.K.C.: Human Behavior Learning and Transfer. CRC Press, Boca Raton (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Mark Eilers
    • 1
  • Claus Möbus
    • 1
  1. 1.Transportation Systems, Learning and Cognitive Systems OFFIS Institute for Information TechnologyC.v.O. UniversityOldenburgGermany

Personalised recommendations